Currently, bone density is the commonly used tool to predict fracture risk and evaluate the effects of treatment on bone but it lacks sensitivity and specificity. More than half of all hip fractures occur in women determined to be without osteoporosis with dual energy X-ray absorptiometry (DXA) with a T-score≦−2.5. Furthermore, most women with osteoporosis as defined by DEXA BMD do not sustain fractures (Delmas P. D., Seeman E. Changes in bone mineral density explain little of the reduction in vertebral or nonvertebral fracture risk with anti-resorptive therapy, Bone 34(4) (2004) p. 599-604). During drug therapy, only a small proportion of the fracture risk reduction is explained by the increase in bone density (Ott S. M., When bone mass fails to predict bone failure, Calcif. Tissue Int. 53(Suppl1) pp. S7-S13). This lack of sensitivity and specificity partly is partly due to the fact that fracture risk is not only determined by bone density but also bone structure (Zebaze R. M. and Seeman S., Measuring Femoral Neck Strength: Lost in Translation?, IBMS BoneKEy 5 (2008) pp. 336-339).
Consequently, techniques such as quantitative computed tomography (CT) and magnetic resonance imaging (MRI) are being used to produce images that are then analysed to predict fracture risk, quantify the effects of drugs and determine the effectiveness of various strategies (such as exercise or diet) for the prevention of bone loss. However, parameters derived from these imaging modalities have not proved to be significantly better than bone density measurement, and cannot presently be used as a substitute for bone density measurement.
The problem with these approaches lies not only in image acquisition but also, and more significantly, in image processing. One of the most important problems with existing techniques for assessing bone structure from such images is their reliance on fixed arbitrary thresholds to identify bone within a region of interest (ROI) and to segment (i.e. separate) bone into its various compartments (usually cortical and trabecular bone). Bone architecture differs from person to person and, for this reason, the use of fix thresholds has different and unpredictable consequences on bones from different individuals. Hence, the use of a fixed threshold can create apparent differences where none exist, or obscure differences when they exist.
Furthermore, current methods employ a simplistic model of bone structure. In particular, these methods treat bone as a structure made of two distinct compartments (cortical and trabecular bone). In fact, the so-called ‘compact’ bone and trabecular (spongy′) bone are extremes of a continuum of variation in porosity from the periosteum to the inner marrow cavity.
In addition, current methods employ so-called ‘phantom calibration’ to determine the density associated with each pixel within an image. This density is then used to identify bone and assess its structure (hence the term quantitative computed tomography (QCT)). Existing calibration procedures are specific to each scanner manufacturer, so bone structure analysis—performed by software embedded in the scanner—is manufacturer specific.